Plot local false sign rate results
Matthew Stephens
2015-10-26
Last updated: 2015-12-15
Code version: c12bd940df26a36d1f462f2dfa0646e58cc24b19
load("../output/dsc-shrink-files/res.RData")
source("../R/set_plot_colors.R")
library(dplyr)
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(ggplot2)
#' @param df dataframe of scores for many methods/scenrios etc
#' @return tall dataframe with columns of scores for each method and the "goldmethod" against which plot is to be made
process_score_for_plotting_against_gold=function(df,PLOTMETHODS=c("ash.n","ash.u","ash.hu"),
GOLDMETHOD="bayes",PLOTSEEDS=1:100,
PLOTSCENARIOS=c("spiky","near-normal","flat-top","skew","big-normal","bimodal"),
PLOTNAMES=PLOTSCENARIOS){
df %<>% filter(seed %in% PLOTSEEDS) %>% filter(scenario %in% PLOTSCENARIOS) %>% filter(method %in% c(PLOTMETHODS,GOLDMETHOD))
df$scenario = factor(df$scenario,levels=PLOTSCENARIOS)
levels(df$scenario)= PLOTNAMES
#create tall version of dataframe
df %<>% dplyr::select(-user.self,-sys.self,-elapsed,-user.child,-sys.child) %>%
reshape2::melt(id.vars=c("method","scenario","seed",".id"),value.name="val")
#separate bayes and remainder
df.bayes = df %>% filter(method==GOLDMETHOD)
df.rest = df %>% filter(method!=GOLDMETHOD)
#join bayes with others, so each line has both the bayes and the non-bayes version
return(inner_join(df.bayes, df.rest, by=c("scenario","seed","variable")))
}
plot_lfsr=function(lfsr,xlab="True lfsr",ylab="Estimated lfsr",xlim=c(0,0.2),ylim=c(0,0.2),legend.position="bottom"){
p=ggplot(lfsr,
aes(val.x,val.y,colour=method.y)) +
facet_grid(. ~ scenario) +
guides(alpha=FALSE) +
geom_abline(colour = "black") +
geom_abline(colour= "red", slope=2) +
xlab(xlab) +
ylab(ylab) +
geom_point(shape=1,size=0.1,alpha=0.2)
p +scale_y_continuous(limits=ylim) +
scale_x_continuous(limits=xlim)
}
lfsr = process_score_for_plotting_against_gold(res$lfsr,PLOTSEEDS=1:100,PLOTMETHODS="ash.n")
lfdr = process_score_for_plotting_against_gold(res$lfdr,PLOTSEEDS=1:100,PLOTMETHODS="ash.n")
p1=plot_lfsr(lfsr,ylim=c(0,1),xlim=c(0,0.2))
p2=plot_lfsr(lfdr,ylim=c(0,1),xlim=c(0,0.2),xlab="True lfdr",ylab="Estimated lfdr")
# cowplot command: plot_grid(p2+theme_gray()+theme(legend.position="none"),p1+theme_gray()+theme(legend.position="none"),nrow=2,align="v")print(p1+theme(legend.position="none",axis.text.x = element_text(size = 8,angle=45))
+coord_equal(ratio=1/5) + colScale)Warning: Removed 95034 rows containing missing values (geom_point).Warning: Removed 86664 rows containing missing values (geom_point).Warning: Removed 87526 rows containing missing values (geom_point).Warning: Removed 81263 rows containing missing values (geom_point).Warning: Removed 65691 rows containing missing values (geom_point).Warning: Removed 68258 rows containing missing values (geom_point).#ggsave("../paper/figures/lfsr_est.pdf",height=3,width=9)print(p2+theme(legend.position="none",axis.text.x = element_text(size = 8,angle=45))
+coord_equal(ratio=1/5) + colScale)Warning: Removed 75034 rows containing missing values (geom_point).Warning: Removed 73449 rows containing missing values (geom_point).Warning: Removed 74231 rows containing missing values (geom_point).Warning: Removed 70760 rows containing missing values (geom_point).Warning: Removed 62698 rows containing missing values (geom_point).Warning: Removed 65160 rows containing missing values (geom_point).